Developing individualized feedback for listening assessment: Combining standard setting and cognitive diagnostic assessment approaches

2021 ◽  
pp. 026553222199547
Author(s):  
Shangchao Min ◽  
Lianzhen He

In this study, we present the development of individualized feedback for a large-scale listening assessment by combining standard setting and cognitive diagnostic assessment (CDA) approaches. We used the performance data from 3358 students’ item-level responses to a field test of a national EFL test primarily intended for tertiary-level EFL learners. The results showed that proficiency classifications and subskill mastery classifications were generally of acceptable reliability, and the two kinds of classifications were in alignment with each other at individual and group levels. The outcome of the study is a set of descriptors that describe each test taker’s ability to understand certain level of oral texts and his or her cognitive performance. The current study, by illustrating the feasibility of combining standard setting and CDA approaches to produce individualized feedback, contributes to the enhancement of score reporting and addresses the long-standing criticism that large-scale language assessments fail to provide individualized feedback to link assessment with instruction.

2021 ◽  
Vol 12 ◽  
Author(s):  
Yan Li ◽  
Miaomiao Zhen ◽  
Jia Liu

Cognitive diagnostic assessment (CDA) has been developed rapidly to provide fine-grained diagnostic feedback on students’ subskills and to provide insights on remedial instructions in specific domains. To date, most cognitive diagnostic studies on reading tests have focused on retrofitting a single booklet from a large-scale assessment (e.g., PISA and PIRLS). Critical issues in CDA involve the scarcity of research to develop diagnostic tests and the lack of reliability and validity evidence. This study explored the development and validation of the Diagnostic Chinese Reading Comprehension Assessment (DCRCA) for primary students under the CDA framework. Reading attributes were synthesized based on a literature review, the national curriculum criteria, the results of expert panel judgments, and student think-aloud protocols. Then, the tentative attributes were used to construct three booklets of reading comprehension items for 2–6 graders at three key stages. The assessment was administered to a large population of students (N = 21,466) in grades 2–6 from 20 schools in a district of Changchun City, China. Q-matrices were compared and refined using the model-data fit and an empirical validation procedure, and five representative cognitive diagnostic models (CDMs) were compared for optimal performance. The fit indices suggested that a six-attribute structure and the G-DINA model were best fitted for the reading comprehension assessment. In addition, diagnostic reliability, construct, internal and external validity results were provided, supporting CDM classifications as reliable, accurate, and useful. Such diagnostic information could be utilized by students, teachers, and administrators of reading programs and instructions.


AERA Open ◽  
2021 ◽  
Vol 7 ◽  
pp. 233285842110608
Author(s):  
Fang Tang ◽  
Peida Zhan

Assessment for learning emphasizes the importance of feedback to promote learning. To explore whether cognitive diagnostic feedback (CDF) promotes learning and whether it is more effective than traditional feedback in promoting learning, this study conducted a quasi-experiment by utilizing a longitudinal cognitive diagnostic assessment to compare the effect of three feedback modes on promoting learning, including CDF, correct–incorrect response feedback (CIRF), and no feedback. The results provided some evidence for the conclusion that CDF can promote students’ learning and is more effective than CIRF in promoting learning, especially in more challenging areas of knowledge.


Author(s):  
Ying Qin

This study extracts the comments from a large scale of Chinese EFL learners' translation corpus to study the taxonomy of translation errors. Two unsupervised machine learning approaches are used to obtain the computational evidences of translation error taxonomy. After manually revision, ten types of English to Chinese (E2C) and eight types Chinese to English (C2E) translation errors are finally confirmed. There probably exists three categories of top-level errors according to the hierarchical clustering results. In addition, three supervised learning methods are applied to automatically recognize the types of errors, among which the highest performance reaches F1 = 0.85 on E2C and F1 = 0.90 on C2E translation. Further comparison to the intuitive or theoretical studies on translation taxonomy shows some phenomenon accompanied by language skill improvement of Chinese learners. Analysis on translation problems based on machine learning provides the objective insight and understanding on the students' translations.


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